Utility of gated SPECT parameters in detection of coronary artery disease in patients with normal perfusion images

Abstract

Background False negative myocardial perfusion images on single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a substantial problem in the interpretation of MPI. To explore possible parameters from gated SPECT that could detected coronary artery disease (CAD) in patients with normal perfusion images, we retrospectively selected patients who underwent a 2-day Tc-99 m MIBI adenosine stress/rest MPI and a subsequent invasive coronary angiography. Gated SPECT parameters, including end systolic volume (ESV), end diastolic volume (EDV), left ventricular ejection fraction (LVEF), and transient ischemic dilation (TID) ratio of patients with and without CAD were compared and assessed for their respective diagnostic performance using receiver operating characteristics (ROC) area under the curve (AUC). Results Among 22 patients with normal perfusion images, 7 patients had CAD. Despite the small number of patients, we found significant differences between the ESV, the EDV, and the LVEF between patients with and without CAD. The analysis of ROC curve showed the stress ESV could excellently detect CAD (AUC = 0.900). The rest ESV, the stress EDV, the rest EDV, the stress LVEF and the rest LVEF could likewise perform well in the detection of CAD (AUC = 0.833, 0.819, 0.790, 0.862, and 0.838, respectively). In contrast, the change in LVEF and the TID ratio (AUC = 0.667 and 0.524, respectively) did not seem as reliable as other parameters. Optimal cutoffs for detection of CAD in patients with normal perfusion images from our study were ≥ 20.0 mL for the stress ESV, ≥ 71.0 mL for the stress EDV, ≤ 66.3 EF units for the stress LVEF, ≥ 18.0 mL for the rest ESV, ≥ 67.0 mL for the rest EDV and ≤ 70.0 EF unit for the rest LVEF. Conclusions Gated SPECT parameters could facilitate detection of CAD in patient with normal perfusion images on Tc-99m MIBI MPI. These parameters should be carefully interpreted to improve diagnostic accuracy and reduce false negative MPI.